Author:
Ding Hui,Pang Zhenjiang,Wang Xueliang,He Yeshen,Tian Peng,Zhang Yiying
Publisher
Springer Nature Singapore
Reference16 articles.
1. Zhang Y., Liu Q.: On IoT intrusion detection based on data augmentation for enhancing learning on unbalanced samples. Future Generation Comput. Syst., 133, 213–227 (2022)
2. Bacevicius, M., Paulauskaite-Taraseviciene, A.: Machine learning algorithms for raw and unbalanced intrusion detection data in a multi-class classification problem. Appl. Sci. 13(12), 7328 (2023)
3. Kamil, W.F., Mohammed, I.J.: Adapted CNN-SMOTE-BGMM deep learning framework for network intrusion detection using unbalanced dataset. Iraqi J. Sci. 64(9) (2023)
4. Balyan, A.K., et al.: A hybrid intrusion detection model using ega-pso and improved random forest method. Sensors 22(16), 5986 (2022)
5. Panigrahi, R., Borah, S., Bhoi, A.K., et al.: A consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets. Mathematics 9(7), 751 (2021)